{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>7</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
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       "      <td>749.12</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0                       1  2        3       4       5      6   7  \\\n",
       "0  162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1  162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2  162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3  162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4  162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('./log.txt',header = None,sep = '\\t')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>74399</td>\n",
       "      <td>5791288</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>2093.17</td>\n",
       "      <td>105.61</td>\n",
       "      <td>834.65</td>\n",
       "      <td>232.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-26 19:31:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13912</td>\n",
       "      <td>1406826</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>146.78</td>\n",
       "      <td>146.78</td>\n",
       "      <td>146.78</td>\n",
       "      <td>146.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-17 11:06:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                     api  count  res_time_sum  res_time_min  \\\n",
       "74399  5791288  /front-api/bill/create      9       2093.17        105.61   \n",
       "13912  1406826  /front-api/bill/create      1        146.78        146.78   \n",
       "\n",
       "       res_time_max  res_time_avg  interval           created_at  \n",
       "74399        834.65         232.0        60  2019-01-26 19:31:42  \n",
       "13912        146.78         146.0        60  2018-11-17 11:06:39  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sample(2) #随机采样,多次执行,数据不一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info() #查看内存占用空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.drop('api',axis = 1) #优化内存,指定axis,指定删除一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>res_time_sum</th>\n",
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       "      <td>0</td>\n",
       "      <td>162542</td>\n",
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       "      <td>2018-11-01 00:00:07</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-02-05 23:22:58\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2019-05-01 00:00:48</td>\n",
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       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
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       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
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       "      <td>153092</td>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
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       "    <tr>\n",
       "      <td>153093</td>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
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       "      <td>179491</td>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179492</td>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179493</td>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
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       "    <tr>\n",
       "      <td>179494</td>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
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       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179495</td>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26407 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "153089  11406128      6       2105.08        125.74        992.46   \n",
       "153090  11406236      7       2579.11         76.55        987.47   \n",
       "153091  11406347      7       1277.79        109.65        236.73   \n",
       "153092  11406446      7       2137.20        131.55        920.52   \n",
       "153093  11406488     13       2948.70         86.42        491.31   \n",
       "...          ...    ...           ...           ...           ...   \n",
       "179491  13438800     11       2783.48         99.24        489.90   \n",
       "179492  13438866     10       1951.10         85.37        529.51   \n",
       "179493  13438917      3        494.17        103.95        211.47   \n",
       "179494  13438981      9       1798.28        101.11        433.30   \n",
       "179495  13439086      6       1017.97         74.45        298.97   \n",
       "\n",
       "        res_time_avg  interval           created_at  \n",
       "153089         350.0        60  2019-05-01 00:00:48  \n",
       "153090         368.0        60  2019-05-01 00:01:48  \n",
       "153091         182.0        60  2019-05-01 00:02:48  \n",
       "153092         305.0        60  2019-05-01 00:03:48  \n",
       "153093         226.0        60  2019-05-01 00:04:48  \n",
       "...              ...       ...                  ...  \n",
       "179491         253.0        60  2019-05-30 23:06:21  \n",
       "179492         195.0        60  2019-05-30 23:07:21  \n",
       "179493         164.0        60  2019-05-30 23:08:21  \n",
       "179494         199.0        60  2019-05-30 23:09:21  \n",
       "179495         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[26407 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2.created_at >= '2019-05-01') &(df2.created_at < '2019-5-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index #当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index = df2['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index = pd.to_datetime(df2.created_at) #将索引转化为时间类型的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>created_at</th>\n",
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       "      <th></th>\n",
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       "      <td>2019-05-01 00:00:48</td>\n",
       "      <td>11406128</td>\n",
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       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
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       "      <td>60</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
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       "      <td>2019-05-01 00:01:48</td>\n",
       "      <td>11406236</td>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "      <td>11406347</td>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "      <td>11475363</td>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "      <td>11475483</td>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "      <td>11475550</td>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "      <td>11475597</td>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "      <td>11475664</td>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                         \n",
       "2019-05-01 00:00:48  11406128      6       2105.08        125.74   \n",
       "2019-05-01 00:01:48  11406236      7       2579.11         76.55   \n",
       "2019-05-01 00:02:48  11406347      7       1277.79        109.65   \n",
       "2019-05-01 00:03:48  11406446      7       2137.20        131.55   \n",
       "2019-05-01 00:04:48  11406488     13       2948.70         86.42   \n",
       "...                       ...    ...           ...           ...   \n",
       "2019-05-01 23:55:49  11475363      6       1083.97         70.85   \n",
       "2019-05-01 23:56:49  11475483      4        840.00        117.31   \n",
       "2019-05-01 23:57:49  11475550      2        295.51        101.71   \n",
       "2019-05-01 23:58:49  11475597      2        431.99         84.43   \n",
       "2019-05-01 23:59:49  11475664      3        428.84        103.58   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval           created_at  \n",
       "created_at                                                                      \n",
       "2019-05-01 00:00:48        992.46         350.0        60  2019-05-01 00:00:48  \n",
       "2019-05-01 00:01:48        987.47         368.0        60  2019-05-01 00:01:48  \n",
       "2019-05-01 00:02:48        236.73         182.0        60  2019-05-01 00:02:48  \n",
       "2019-05-01 00:03:48        920.52         305.0        60  2019-05-01 00:03:48  \n",
       "2019-05-01 00:04:48        491.31         226.0        60  2019-05-01 00:04:48  \n",
       "...                           ...           ...       ...                  ...  \n",
       "2019-05-01 23:55:49        262.22         180.0        60  2019-05-01 23:55:49  \n",
       "2019-05-01 23:56:49        382.63         210.0        60  2019-05-01 23:56:49  \n",
       "2019-05-01 23:57:49        193.80         147.0        60  2019-05-01 23:57:49  \n",
       "2019-05-01 23:58:49        347.56         215.0        60  2019-05-01 23:58:49  \n",
       "2019-05-01 23:59:49        206.57         142.0        60  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.drop(['id','interval'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>res_time_min</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3['count'].hist()  #初步分析count,直方图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#表示接口调用分布情况,大部分都在10次以内,反映出每分钟调用的次数的分布情况\n",
    "df3['count'].hist(bins = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据,绘制一天时段的接口调用情况\n",
    "df3['2019-5-1']['count'].plot()\n",
    "plt.show()\n",
    "#凌晨时间无人访问,下午2-3点是第一个访问高峰,晚上8-9点是第二个访问高峰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用count重新采样,用一个小时进行采样,没有那么多数据点,图像比较平滑\n",
    "df4 = df3['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-05-01 00:00:00</td>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 01:00:00</td>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 02:00:00</td>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 05:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 06:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 07:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 08:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 09:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 10:00:00</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 11:00:00</td>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 12:00:00</td>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 13:00:00</td>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 14:00:00</td>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 15:00:00</td>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 16:00:00</td>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 17:00:00</td>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 18:00:00</td>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 19:00:00</td>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 20:00:00</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 21:00:00</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 22:00:00</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 23:00:00</td>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4[['count']].resample('1H').mean()\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#折线图与直方图,可以看到业务的高峰时段在什么地方,分不清具体时间,绘制柱状图\n",
    "plt.figure(figsize=(10,3))#单位是英寸\n",
    "df4['count'].plot(kind = 'bar')\n",
    "plt.xticks(rotation = 60)#w文字旋转角度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LwX+2iS5mgL+NiDMppXLn/wKpOV5GKEmZcgpFkjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlKn/A6+SRiLXU0nxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图 分析有没有异常时段,访问接口过于频繁,可能是黑客潮水攻击\n",
    "df3['2019-5-1'][['count']].boxplot(showmeans = True,meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3['count']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 某一天的响应时间,平均响应时间\n",
    "df3['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x233bad17488>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3['2019-5-1'][['res_time_avg']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\think\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df3['2019-5-1']\n",
    "df5[df3['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  2019-05-01 00:34:48\t1\t1694.47\t1694.47\t1694.47\t1694.0\t2019-05-01 00:34:48  异常值\n",
    "df3['2019-5-1'][['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df3['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum','res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 业务高峰时段 下午2-3点  晚上7-8点  相应时间都是上升的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3['2019-5-4':'2019-5-15']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='created_at', length=865)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每天的情况都差不多,查看周末与平常\n",
    "df3['2019-5-2'].index.weekday #0 代表星期一 1 代表星期二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['weekday'] = df3.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  \n",
       "created_at                                                       \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  weekday  weekend  \n",
       "created_at                                                                \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断是否是周末,是不是5/6\n",
    "df3['weekend'] = df3['weekday'].isin({5,6})\n",
    "df3.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对weekend 进行分组,对count列求平均值\n",
    "df3.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#周末调用平均次数多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#周末哪个时段调用次数比较高\n",
    "df3.groupby(['weekend',df3.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#周末和非周末具体时间对比,绘制成图形,否则不知观\n",
    "df3.groupby(['weekend',df3.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#周末和非周末的数据叠加\n",
    "df3.groupby(['weekend',df3.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.groupby(['weekend',df3.index.hour])['count'].mean().unstack(level=0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
